Investigation\non Junction Contacts of Semiconducting\nCarbon Nanotube Networks Using Conductive Atomic Force Microscopy

Semiconductor\nsingle-walled carbon nanotube (s-SWNT) networks have\ngained prominence in electronic devices due to their cost-effectiveness,\nrelatively production-naturality, and satisfactory performance. Configuration,\ndensity, and resistance of SWNT–SWNT junctions are considered\ncrucial factors influencing the overall conductivity of s-SWNT networks.\nIn this study, we present a method for inferring the lower bounds\nof the SWNT–SWNT junction resistance in s-SWNT networks based\non conductive atomic force microscopy TUNA images. This method further\nenables the proposal of a classification for SWNT–SWNT junctions\nbased on the current behavior relative to their surroundings. The\nthree types of SWNT–SWNT junctions are denoted as (i) true\ncontact (T), (ii) poor contact (P), and (iii) false contact (F). Of\nthem, the true and poor contacts, respectively, represent good and\npoor electrical contact for the subject SWNT–SWNT junctions\nwhose electrical conductivity hardly improves under external tip pressure,\nwhile that of the false contact can be further improved by external\npressure. Statistical analysis demonstrates that while T-type junctions\nmake a significant contribution to network conductivity, their proportion\naccounts for only approximately 40%. The P-type and F-type junctions,\nwhich constitute over 60% of the total, may be a contributing factor\nthat constrains the overall conductivity of the s-SWNT networks. The\nheight ratio of the junction to the sum of two SWNTs was also observed\nto exhibit variations among the three types. Finally, we propose a\nthree-dimensional model to elucidate the formation mechanism underlying\neach type of junction. The present study provides insights into the\nperformance of spontaneous contacts between s-SWNTs in the networks,\nand the systematic image acquisition and junction classification processes\nmay provide support for future advancements in these networks.